Bibliographic record
Abstract
This chapter argues that research ethics boards are not neutral arbitrators assessing the ethical components of research involving human participants, but in some cases their interventions in research design can perpetuate prevailing legal, political, and cultural norms in ways that further marginalize migrant populations, especially when migrants’ practices do not conform to such norms. I examine the ethics review of my study on Mandarin-language ride-hailing services in Metro Vancouver in the Province of British Columbia and discuss how the research ethics board’s requests for revisions to my study were influenced by cultural norms about Asians, and in particular, Yellow Peril ideologies circulating in dominant English-language media, which reproduce the stereotypical association of Mandarin-speaking migrants with criminality. My analysis is informed by my position as an international student from the Mandarin-speaking community who uses the ride-hailing services. My aim was to learn about the ride-hailing from the perspectives of the drivers and passengers as migrants in a city with a long history of anti-Asian legislation. The chapter claims that ethics boards’ standard ethical procedures can reinforce Yellow Peril discourses and restrict researchers’ ability to gain insights into why these populations set up services like ride-hailing in the first place.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".